On generating utility functions in Stochastic Multicriteria Acceptability Analysis

Dias, Luís C.; Vetschera, Rudolf

doi:10.1016/j.ejor.2019.04.031

Cited by 19 publications

(9 citation statements)

References 24 publications

(37 reference statements)

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“…A widely used method for generating general monotone value functions is to assign marginal values to distinct performance values by sorting random values drawn from a uniform distribution [21]. In a recent study by [8], this method for generating general monotone value functions is found to bias the obtained value functions towards a certain shape, especially when the distribution of performance values is not uniform. Recall that we observe the imbalanced distribution of performance values in Figure 1.…”

Section: Simulating Decision Policies With Non-linear Value Functionsmentioning

confidence: 99%

A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples

Liu

Kadziński

Liao

et al. 2020

European Journal of Operational Research

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We present a preference learning framework for multiple criteria sorting. We consider sorting procedures applying an additive value model with diverse types of marginal value functions (including linear, piecewise-linear, splined, and general monotone ones) under a unified analytical framework. Differently from the existing sorting methods that infer a preference model from crisp decision examples, where each reference alternative is assigned to a unique class, our framework allows to consider valued assignment examples in which a reference alternative can be classified into multiple classes with respective credibility degrees. We propose an optimization model for constructing a preference model from such valued examples by maximizing the credible consistency among reference alternatives. To improve the predictive ability of the constructed model on new instances, we employ the regularization techniques. Moreover, to enhance the capability of addressing large-scale datasets, we introduce a state-of-the-art algorithm that is widely used in the machine learning community to solve the proposed optimization model in a computationally efficient way. Using the constructed additive value model, we determine both crisp and valued assignments for non-reference alternatives. Moreover, we allow the Decision Maker to prioritize importance of classes and give the method a flexibility to adjust classification performance across classes according to the specified priorities. The practical usefulness of the analytical framework is demonstrated on a real-world dataset by comparing it to several existing sorting methods.

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Section: Simulating Decision Policies With Non-linear Value Functionsmentioning

confidence: 99%

A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples

Liu

Kadziński

Liao

et al. 2020

European Journal of Operational Research

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show abstract

“…However, in general there can be multiple other ρ mappings preserving linearity. To analyze the results corresponding to the multiple ρ mappings that preserve linearity it is possible to follow a stochastic approach, inspired by the Stochastic Multi-attribute Acceptability Analysis (SMAA) methods [36,[49][50][51].…”

Section: Stochastic Analysismentioning

confidence: 99%

Using Different Qualitative Scales in a Multi-Criteria Decision-Making Procedure

2020

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Many decision problems manage linguistic information assessed through several ordered qualitative scales. In these contexts, the main problem arising is how to aggregate this qualitative information. In this paper, we present a multi-criteria decision-making procedure that ranks a set of alternatives assessed by means of a specific ordered qualitative scale for each criterion. These ordered qualitative scales can be non-uniform and be formed by a different number of linguistic terms. The proposed procedure follows an ordinal approach by means of the notion of ordinal proximity measure that assigns an ordinal degree of proximity to each pair of linguistic terms of the qualitative scales. To manage the ordinal degree of proximity from different ordered qualitative scales, we provide a homogenization process. We also introduce a stochastic approach to assess the robustness of the conclusions.

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“…We therefore use a different approach, which is similar to the bisection method for arbitrary utility functions described in Dias and Vetschera (2018). In contrast to Dias and Vetschera (2018), we here generate utility values for a large number of equidistant points, and not for a small number of predefined attribute values, and we only generate functions of a specified shape.…”

Section: Generation Of Utility Functionsmentioning

confidence: 99%

Multiple local optima in Zeuthen–Hicks bargaining: an analysis of different preference models

Dias

Vetschera

2019

EURO Journal on Decision Processes

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Zeuthen-Hicks bargaining provides a dynamic model that explains how two parties in a negotiation make concessions to reach the Nash bargaining solution. However, it is not clear whether this process will always reach the global optimum corresponding to the Nash bargaining solution, or could end at a local optimum, or even in disagreement. In this paper, we analyze different types of utility functions, both analytically and in a computational study, to determine under which circumstances convergence to the Nash bargaining solution will be achieved. We show that non-standard preferences, involving e.g. reference point effects, might indeed lead to multiple local optima of the Nash bargaining objective function and thus failure of the bargaining process. This occurs more often if expectations of parties are mutually incompatible.

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On generating utility functions in Stochastic Multicriteria Acceptability Analysis

Cited by 19 publications

References 24 publications

A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples

A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples

Using Different Qualitative Scales in a Multi-Criteria Decision-Making Procedure

Multiple local optima in Zeuthen–Hicks bargaining: an analysis of different preference models

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